Savitzky-Golay 필터와 미분을 활용한 LSTM 기반 지하수 수위 예측 모델의 성능 비교

Performance Comparison of LSTM-Based Groundwater Level Prediction Model Using Savitzky-Golay Filter and Differential Method

  • Keun-San Song (Division of Biomedical Engineering, Konyang University) ;
  • Young-Jin Song (Division of Biomedical Engineering, Konyang University)
  • 투고 : 2023.08.23
  • 심사 : 2023.09.12
  • 발행 : 2023.09.30

초록

In water resource management, data prediction is performed using artificial intelligence, and companies, governments, and institutions continue to attempt to efficiently manage resources through this. LSTM is a model specialized for processing time series data, which can identify data patterns that change over time and has been attempted to predict groundwater level data. However, groundwater level data can cause sen-sor errors, missing values, or outliers, and these problems can degrade the performance of the LSTM model, and there is a need to improve data quality by processing them in the pretreatment stage. Therefore, in pre-dicting groundwater data, we will compare the LSTM model with the MSE and the model after normaliza-tion through distribution, and discuss the important process of analysis and data preprocessing according to the comparison results and changes in the results.

키워드

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